Study Finds Zero-Shot AI Models Cannot Reliably Predict Stock Movements from Financial News
A new arXiv preprint finds that large language models using zero-shot natural language processing consistently fail to outperform simple baseline models when predicting short-term stock price movements from financial news. The researchers tested a structured pipeline combining zero-shot natural language inference with temporal aggregation across multiple models and prediction horizons, finding especially weak performance on predicting negative price movements. The findings suggest fundamental structural limits in mapping news sentiment to short-term price dynamics, while pointing to explainability frameworks as a practical tool for identifying when AI predictions can and cannot be trusted.
Researchers have published a preprint on arXiv examining whether financial news can reliably predict short-term stock movements using zero-shot large language models — that is, models applied without domain-specific financial training. Their pipeline incorporated zero-shot natural language inference, temporal aggregation, and explicit modeling of recency and event-dependent impact horizons across articles. Testing across multiple models and prediction horizons, the study found that zero-shot approaches consistently failed to beat simple baselines, with particularly poor performance in identifying negative price movements. Alongside the predictive analysis, the team introduced a multi-layered explainability framework capable of linking model predictions to token-level, article-level, and aggregate evidence, and generating natural language rationales. Notably, even when predictive accuracy was low, the explainability signals reliably distinguished between more and less trustworthy predictions, suggesting practical utility as a decision-support tool. The authors argue these results reveal deeper structural limitations in using news sentiment for short-term price forecasting and advocate for AI systems that prioritize transparency and uncertainty awareness over raw predictive claims. The paper has been submitted to arXiv and its DOI registration is pending.
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The study is a preprint and has not yet undergone peer review, which limits confidence in its findings.
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- arXiv cs.CLCenter
Can News Predict the Market? Limits of Zero-Shot Financial NLP and the Role of Explainable AI
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